Earthquake Modeling Innovations

1. Earthquake Modeling Innovations Bill Graf, P.E. Dames & Moore, Los Angeles Ron Kozlowski, FCAS, MAAA Tillinghast-Towers Perrin, San Francisco Don Windeler Risk Management Solutions, Menlo Park 1998 CAS Special Interest Seminar – Catastrophe Issues

2. Earthquake Modeling Innovations Bill Graf Dames & Moore, Los Angeles

3. Outline • Current State-of-the-Art • Future of Cat Models • Improvements in hazard & vulnerability • Treatment of uncertainty • Calibration Challenges • Risk Management Tools • Other innovations

4. Background – Current State-of-the-Art Earthquake catastrophe models assist in managing earthquake risks by predicting the frequency and severity of insured earthquake losses to buildings, contents and inventory.

5. Current State of the Art Model Components – • Seismic hazards • Building vulnerability • Allocation Models Who bears what loss cost? • Risk Management Tools • Putting the science & engineering to work...

6. Cat Model Schematic Building class data Building location data Building value data Seismology Engineering Actuarial Science Seismic Hazard Damage Functions Insurance/ Allocation m, s m, s m, s Judgment, Market Other factors Decisions

7. Frequency Severity [$] Background – Current State-of-the-Art • Current Cat models produce reasonable hazard simulations, fair frequency estimates, and fair structural damage projections for ground shaking hazards.

8. Seismic Hazards • We cannot predict the next earthquake’s location or magnitude • We can estimate the long-term frequencies of earthquakes of a given size (M) in each region ?

9. Background – Current State-of-the-Art • Seismic Hazards • Ground shaking • Ground conditions • ‘Special’ hazards • fault rupture • liquefaction • landslide • tsunami • fire

10. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Seismic Hazards Northridge M6.4 PGA [g] 2 1 • Given the occurrence of an earthquake, we can reasonably estimate ground shaking levels 0.1 0.01 0.001 0.1 1 10 100 300 Source Distance [Km] Comparison of Recorded Peak Accelerations With an Mean Attenuation Relationship

11. Seismic Hazards • The occurrence, severity, distribution (and cost) of effects from: • tsunami inundation, • soil liquefaction, • surface fault rupture, • landslide or • fire from earthquake are much more difficult to assess than ‘shake’ damage.

12. Site Spectrum (SA) Building Capacity Curves Displacement Background – Current State-of-the-Art Vulnerability Models • ATC-13 • NIBS & FEMA Engineering-based building damage models Acceleration

13. Background – Current State-of-the-Art • Insurance Risk Management Tools • Standard: PML @ ‘n’ years, AAL, mapping • Needed: • Tools to identify which properties contribute to high catastrophic losses • Tools to see ‘where to play’ on a given risk • Tools to integrate business risks and natural hazards risks

14. Background – Current State-of-the-Art ‘Cat’ models should not be used blindly, but with a real understanding of their strengths and weaknesses

15. Building Vulnerability • ATC-13, Wiggins, Steinbrugge, Theil-Tzutty... • PGA ^ MMI ^ damage (m & s) • New building damage models have been developed for NIBS and FEMA • NIBS Standardized Earthquake Loss Estimation Methodology • FEMA 273 “Seismic Rehabilitation of Buildings”

16. Building Vulnerability F=ma • The new models project structural response (drift and acceleration) as a function of a measurable ground motion quantity (spectral acceleration & period). not MMI! • Damage is estimated based on judgmental correlation between structural response and repair cost. drift roof floor ground

17. Building Capacity Curves Zone 4 Zone 3 Zone 2 Spectral Acceleration Site Ground Motion Spectrum Drift 4 Drift 3 Drift 2 Roof Displacement Building Vulnerability – NIBS Engineering Judgment Used For: • Estimates of ductility & damping • Design strength and overstrength • Building Fundamental Period • Drift ratios defining damage states • Correlation of damage to drift and acceleration

18. Building Vulnerability – NIBS NIBS or FEMA 273 Building Damage Models: Advantages Current Limitations • Use of engineering-based models • Many layers of engineering • Measurable ground motion input judgment implicit (spectral acceleration, site soils, duration) • Largely uncalibrated • Allows damage correlation with (some checking vs. Northridge) detailed engineering models • Insurer data is general, with little structural detail

19. Spectral Acceleration Early Failure Roof Displacement Building Vulnerability Limitations of [Any] Models – vertical irregularity – plan irregularity (torsion) – nonductile structures Weak/Soft Story Damage concentrated. Low damage ratio, but possible total loss

20. Building Vulnerability – NIBS Engineering-based models (NIBS, FEMA) represent a significant improvement, compared to simple statistical models. They will be more widely used (and calibrated) in the future. However, at present, these models lack calibration. The improvement engineering-based offer in loss- prediction is limited by the quality of insurer structural data.

21. Needed: Good Inventory Data! Without good structural vulnerability data and replacement value data breakdowns, the new building damage-function models can be no more accurate than the old models. GIGO: The greatest improvement in Cat model results is in the hands of the insurers and brokers - gathering and maintaining good inventory data(vulnerability and value) Data Chain: Owner ^Broker ^ Insurer ^ Reinsurer ^ Regulator

22. Data Needed to Take Advantage of New Damage Models High Acceleration (More damage!) • Detailed structural vulnerability info: • Structural class data for more • detailed sub-categories • • Year of construction and/or design code • • Building-specific design features • – vertical and plan irregularity, redundancy, design • level, level of seismic detailing • Value data for structure & contents or TI by floor: • Drift & acceleration vary floor-by-floor (EQSL example) Low Acceleration (Less damage)

23. Who Can Assess Quake Vulnerability? Best: Civil Engineer/Structural Engineer Loss control staff? Fair: Architect or Contractor (Need Training) Poor:Broker (No engineering training)

24. When to Assess Quake Vulnerability? Best: Before admitting to portfolio – difficult, given time & cost constraints Else: Prior to renewal – provide incentives to insured to obtain better data in insurance co. format

25. The Calibration Challenge • Optimum: “Controlled Laboratory Experiment” • Known materials & design (tested/calibrated) • Measured ‘inputs’ (ground motion; other hazards) • Measured ‘outputs’ (not subjective) – displacement, acceleration, force, stress – cost to repair to pre-earthquake state • Repeatable, with quantifiable variation

26. The Calibration Challenge • Actual: Post-Earthquake Reconnaissance • Unknown materials & design (visual observation) • Poorly known ‘inputs’ (local ground motion highly variable) • No measured ‘outputs’ (inferred - subjective) – displacement, acceleration, force, stress – cost to repair subject to adjustment process • Not repeatable • Large & unknown variation

27. The Calibration Challenge Damage observations (structural engineers) Ground motion [CDMG, USGS] Loss cost data (adjustors) Venn Diagram

28. The Calibration Challenge • Suggestions for Loss-Experience Data: • Structural engineering inspection of properties with significant claims • Uniform, industry-wide data collection methods • More strong motion instrumentation – Require instrumentation of all large structures – Instrumentation of all ZIPs (at Post Office) by USGS in CA, WA... • Dissemination of data through FEMA or other appropriate government agency

29. The Calibration Challenge We must find ways to share loss-experience data. This data must be properly collected, processed and disseminated if we are to learn and advance in insurance, in engineering, and in governmental disaster planning. There is no competitive advantage in collective ignorance. It is easy to share data and still protect the privacy of the insured

30. Treatment of Uncertainty The systematic and comprehensive treatment of uncertainties in Cat models is essential for insurers and reinsurers

31. 5 Broker Data 4 Confirmed Class Some Engineering Review 3 Detailed Engineering Analysis Coefficient of Variation 2 1 0 0.001 0.01 0.1 1 Damage Ratio Treatment of Uncertainty Building Damage Function Variability Also – misclassification of structures

32. Treatment of Uncertainty Ground Shaking Hazard Variability For Y = PGA or SA Std Dev(lnY) = 0.5 - 0.7 (on known geology) 0.6Y ≤ Y ≤ 1.8Y (± 1 Std Dev)

33. Treatment of Uncertainty • Building Replacement Value Errors • Insured value vs. replacement value • Exclusion of land value [what is damagable?] • Value errors: correlated and uncorrelated • – Correlated: old values or poor method • – Uncorrelated: special architectural features • Needed: systematic handling of value ($) errors

34. Risk Management Tools The insurance industry asks one set of questions. The seismologist and the structural engineer tend to answer a different set of questions. Risk management tools should be where Cat modelers apply the science and engineering to providing the best possible answer to the insurance industry questions. Insured loss? PGA, SA, component overstress, drift

35. “Bad” “Good” Before Adding ‘A’ After Adding ‘A’ Frequency Frequency Portfolio Loss [$] Portfolio Loss [$] Risk Management Tools Harnessing the power of the Cat models • For underwriters • For loss control/surveillance

36. Risk Management Tools • Risk diversification indices • Underwriting decisions based on marginal portfolio impacts • True risk-based rates • Integration of insurance with other loss-reduction options

37. On The Horizon • Faster computers • Friendlier models • Web-based tools – Web access to engineering staff, geologists... • Better hazard models • Better damage models • More thorough treatment of uncertainty • Higher geologic data resolution • More & better vulnerability data collection • Closer collaboration between Cat modelers, • insurers, government

38. On The Horizon • Time-dependent business models – track accumulation of assets, with random losses – include the adjustment process – direct answers to insurer solvency question • Integration of natural hazard risk and business risk • Rapid post-earthquake simulation and response management

39. Summary of Main Points • Current State-of-the-Art • Future of Cat Models • Improvements in hazard & vulnerability • Treatment of uncertainty • Calibration Challenges • Sharing loss data • Risk Management Tools • Optimum Use of Cat Models • Other innovations

40. In Conclusion We see a growing reliance on ‘Cat’ models to assess the frequency and range of future risk outcomes, as well as loss costs. Intelligently used, Cat models will improve the decisions we make on risks from natural hazards.